Method for examining prognosis of breast cancer

ABSTRACT

Disclosed is a method for examining prognosis of breast cancer including the steps of: (A) extracting RNA from a specimen collected from a subject, (B) preparing a determination sample using the extracted RNA, (C) determining the expression level of each gene in the specific gene groups using the obtained determination sample, (D) analyzing the expression level of the determined each gene, and (E) examining prognosis of breast cancer, based on the obtained analysis result are performed.

TECHNICAL FIELD

The present invention relates to a method for examining prognosis of breast cancer.

BACKGROUND ART

In about ⅔ cases of primary breast cancer cases, estrogen receptor (ER) is present in breast cancer cells (referred to as “ER-positive”). In ER-positive breast cancer cells, binding of estrogen to ER contributes to cell proliferation.

Therefore, in the treatment for node-negative and ER-positive breast cancer patients, hormonal therapy targeting ER plays an important role.

In hormonal therapy for the node-negative and ER-positive breast cancer patients, metastasis of breast cancer and recurrence are suppressed by, for example, administering an antiestrogen such as tamoxifen to the patients, thereby blocking the binding of estrogen to ER in the breast cancer cells to suppress proliferation of breast cancer cells. In the case where the above patients are treated with the above hormonal therapy, most of the patients show comparatively good prognosis.

However, about 20% of the above patients may have a recurrence of breast cancer.

Therefore, in order to reduce the recurrence rate, most of the node-negative and ER-positive breast cancer patients are treated with not only hormonal therapy but also adjuvant chemotherapy at present, even though chemotherapy is considered to be unnecessary for the node-negative and ER-positive breast cancer patients in most cases.

Thus, it seems to be important to predict the prognosis of the node-negative and ER-positive breast cancer patients in order to provide adjuvant chemotherapy only to patients who are at high risk for recurrence.

Recently, based on an analysis of comprehensive gene expression profile, prediction of breast cancer prognosis in a breast cancer patient has been attempted (see, for example, Patent Literatures 1 and 2, Non Patent Literature 1 and the like).

The Patent Literature 1 describes a method for classifying a breast cancer patient into a patient having “no distant metastases within five years from the time of initial diagnosis” or a patient having “distant metastases within five years from the time of initial diagnosis”, using gene markers identified by using tumor samples of 117 breast cancer patients, based on the difference between the gene marker expression in a cell sample of a breast cancer patient and the gene marker expression in a control. In addition, the Patent Literature 1 describes that as the gene markers, a gene marker capable of distinguishing the presence or absence of ER, a gene marker capable of distinguishing between tumors having a mutation of BRCA1 gene and sporadic tumors, and a gene marker capable of distinguishing between a patient having “no distant metastases within five years from the time of initial diagnosis” and a patient having “distant metastases within five years from the time of initial diagnosis” are used.

In addition, the Patent Literature 2 describes a method for diagnosing prognosis, comprising the steps of obtaining gene expression profile in the biological samples of breast cancer patients, with the use of 76 genes providing an indication of prognosis, the genes being identified by using tumor samples of 286 node-negative breast cancer patients, and comparing the expression level obtained from the gene expression profile with the predetermined cut-off levels.

Furthermore, the Non Patent Literature 1 describes a method for predicting a prognosis, wherein cases of breast cancer conventionally classified as histological grade 2 is further classified into a high-risk group for recurrence and a low-risk group for recurrence by using Genomic Grade Index (GGI) based on 97 genes, the genes being identified by using 189 cases of invasive breast cancer patients and three known gene expression datasets of breast cancer.

SUMMARY OF INVENTION

However, since these methods are affected by the difference between institutions examined, the difference between races and the like, these methods cannot always properly predict prognosis at present.

The present invention has been made in view of the above conventional arts, and an object of the present invention is to provide a method for examining prognosis of breast cancer, which can properly predict prognosis.

More specifically, the present invention relates to:

{1} a method for examining prognosis of breast cancer comprising the steps of:

(A) extracting RNA from a specimen collected from a subject,

(B) preparing a determination sample using the RNA extracted in the step (A),

(C) determining the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2 using the determination sample obtained in the step (B),

(D) analyzing the expression level of each gene determined in the step (C), and

(E) examining prognosis of breast cancer, based on the analysis result obtained in the step (D);

{2} the method for examining prognosis of breast cancer according to the above item {1}, wherein the expression level is analyzed by using a classification method, in the step (D);

{3} the method for examining prognosis of breast cancer according to the above item {2}, wherein the classification method is Between-group analysis;

{4} the method for examining prognosis of breast cancer according to the above item {1}, comprising the steps of:

calculating solution D of a discriminant using the expression level and the discriminant represented by the following formula (1):

$\begin{matrix} {D = {\frac{\sum\limits_{i}{w_{i} \times X_{i}}}{\sum\limits_{i}X_{i}} - 0.0061}} & (1) \end{matrix}$ in the step (D), and

predicting poor prognosis when the solution D of the discriminant is a positive value, and good prognosis when the solution D is 0 or a negative value, in the step (E),

wherein i in the formula (1) shows the gene number provided to the nucleic acid described in Table 1-1 and Table 1-2, w_(i) in the formula (1) shows a weight coefficient corresponding to the nucleic acid with gene number i described in Table 1-1 and Table 1-2, X_(i) in the formula (1) shows a normalized expression level which is obtained by normalization using the following formula (2): X _(i) =y _(i)+abs[round{min(y _(ij))−1}]  (2) and

$\sum\limits_{i}\;$ shows the summation of each nucleic acid, and wherein j in the formula (2) shows the specimen number provided to each specimen, y_(ij) in the formula (2) shows the standardized expression level in a specimen with specimen number j of a gene corresponding to the nucleic acid with gene number i, min in the formula (2) shows the minimum value of the value in parentheses, round in the formula (2) shows the value obtained by rounding the value in parentheses to the nearest whole number, abs in the formula (2) shows the absolute value of the value in parentheses, y_(i) in the formula (2) shows a standardized expression level of a gene corresponding to the nucleic acid with gene number i, the standardized expression level being obtained by standardization using the following formula (3): y _(i) =x _(i) −u _(i)  (3) wherein x_(i) in the formula (3) shows the expression level of a gene corresponding to the nucleic acid with gene number i, and u_(i) in the formula (3) shows the average value of specimens of the expression level of a gene corresponding to the nucleic acid with gene number i; {5} the method for examining prognosis of breast cancer according to the above item {1}, wherein the expression level is analyzed by a hierarchical cluster analysis, in the step (D); {6} the method for examining prognosis of breast cancer according to the above item {1}, wherein the expression level is analyzed by a scoring method, in the step (D); and {7} the method for examining prognosis of breast cancer according to any of the above item {1}, wherein the expression level is determined by using a microarray having at least the nucleic acid described in Table 1-1 and Table 1-2.

According to the method for examining prognosis of breast cancer of the present invention, an excellent effect such that prognosis can be properly predicted is exhibited.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a graph showing the result of examining a relationship between the number of probes and the area under the curve of ROC curve in Example 1.

FIG. 2 is a chart showing the result of comparing the prediction result by a discriminant with the observation result for 105 cases of breast cancer patients in Example 2.

FIG. 3 is a graph showing the result of examining a relationship between the period after surgery and the recurrence-free survival rate in Example 2.

FIG. 4 is a dendrogram showing the result of performing a hierarchical cluster analysis of the expression level data of genes corresponding to the nucleic acids (probe sets) in each of 105 cases of breast cancer patients in Example 3.

FIG. 5 is a scatter plot of the first principal component score and the second principal component score calculated based on the expression level data of each of 105 cases of breast cancer patients in Example 4.

DESCRIPTION OF EMBODIMENTS

The method for examining prognosis of breast cancer of the present invention includes the steps of:

(A) extracting RNA from a specimen collected from a subject,

(B) preparing a determination sample using the RNA extracted in the step (A),

(C) determining the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2 using the determination sample obtained in the step (B),

(D) analyzing the expression level determined in the step (C), and

(E) examining prognosis of breast cancer, based on the analysis result obtained in the step (D).

TABLE 1-1 GenBank Weight Gene Number Probe Set. ID Gene Symbol UniGene.ID Accession Number Coefficient 1 219306_at KIF15 Hs.646856 NM_020242 0.5960 2 218585_s_at DTL Hs.656473 NM_016448 1.4097 3 221677_s_at DONSON Hs.436341 AF232674 0.4683 4 201088_at KPNA2 Hs.594238 NM_002266 1.0480 5 209034_at PNRC1 Hs.75969 AF279899 −1.4059 6 202610_s_at MED14 Hs.407604 AF135802 −0.0393 7 218906_x_at KLC2 Hs.280792 NM_022822 0.0880 8 212723_at JMJD6 Hs.514505 AK021780 0.3141 9 222231_s_at LRRC59 Hs.370927 AK025328 0.6264 10 208838_at CAND1 Hs.546407 AB020636 0.2207 11 218039_at NUSAP1 Hs.615092 NM_016359 1.5846 12 209472_at CCBL2 Hs.481898 BC000819 −1.7235 13 212898_at KIAA0406 Hs.655481 AB007866 0.1172 14 202620_s_at PLOD2 Hs.477866 NM_000935 1.3605 15 201059_at CTTN Hs.596164 NM_005231 0.3591 16 201841_s_at HSPB1 Hs.520973 NM_001540 1.2420 17 203755_at BUB1B Hs.631699 NM_001211 0.9909 18 211750_x_at TUBA1C Hs.719091 BC005946 0.0145 19 38158_at ESPL1 Hs.153479 D79987 0.5325 20 204709_s_at KIF23 Hs.270845 NM_004856 0.0798 21 201589_at SMC1A Hs.211602 D80000 0.3106 22 218460_at HEATR2 Hs.535896 NM_017802 0.0198 23 207430_s_at MSMB Hs.255462 NM_002443 1.9177 24 212139_at GCN1L1 Hs.298716 D86973 −0.0501 25 211596_s_at LRIG1 Hs.518055 AB050468 −2.0999 26 212160_at XPOT Hs.85951 AI984005 0.3461 27 219238_at PIGV Hs.259605 NM_017837 −1.2689 28 203432_at TMPO Hs.11355 AW272611 0.4665 29 201377_at UBAP2L Hs.490551 NM_014847 0.1269 30 218875_s_at FBXO5 Hs.520506 NM_012177 0.1012 31 221922_at GPSM2 Hs.584901 AW195581 0.4423 32 218727_at SLC38A7 Hs.10499 NM_018231 −0.0411 33 207469_s_at PIR Hs.495728 NM_003662 0.8827 34 218483_s_at C11orf60 Hs.533738 NM_020153 −1.3198 35 204641_at NEK2 Hs.153704 NM_002497 1.5825 36 219502_at NEIL3 Hs.405467 NM_018248 −0.1883 37 209054_s_at WHSC1 Hs.113876 AF083389 0.0465 38 220318_at EPN3 Hs.670090 NM_017957 0.3073 39 210297_s_at MSMB Hs.255462 U22178 1.6681 40 209186_at ATP2A2 Hs.506759 M23114 0.2014 41 219787_s_at ECT2 Hs.518299 NM_018098 0.8181 42 45633_at GINS3 Hs.47125 AI421812 −0.2363 43 200848_at AHCYL1 Hs.705418 AA479488 −1.5895 44 200822_x_at TPI1 Hs.524219 NM_000365 0.0814 45 211072_x_at TUBA1B Hs.719075 BC006481 0.0380 46 200811_at CIRBP Hs.634522 NM_001280 −1.4620 47 202864_s_at SP100 Hs.369056 NM_003113 −1.3947 48 202154_x_at TUBB3 Hs.511743 NM_006086 0.1241 49 213152_s_at SFRS2B Hs.476680 AI343248 −1.2495 50 209368_at EPHX2 Hs.212088 AF233336 −1.8835

TABLE 1-2 GenBank Weight Gene Number Probe Set. ID Gene Symbol UniGene.ID Accession Number Coefficient 51 211058_x_at TUBA1B Hs.719075 BC006379 0.0646 52 209251_x_at TUBA1C Hs.719091 BC004949 0.0453 53 213646_x_at TUBA1B Hs.719075 BE300252 0.0396 54 204540_at EEF1A2 Hs.433839 NM_001958 1.8487 55 202026_at SDHD Hs.719164 NM_003002 −1.3587 56 201090_x_at TUBA1B Hs.719075 NM_006082 0.0733 57 213119_at SLC36A1 Hs.269004 AW058600 0.0680 58 217840_at DDX41 Hs.484288 NM_016222 0.0313 59 206559_x_at EEF1A1 — NM_001403 −0.9727 60 202066_at PPF1A1 Hs.530749 AA195259 0.7385 61 203108_at GPRC5A Hs.631733 NM_003979 1.0799 62 218697_at NCKIPSD Hs.655006 NM_016453 −0.0693 63 222039_at KIF18B Hs.135094 AA292789 0.6820 64 202069_s_at IDH3A Hs.591110 AI826060 0.2302 65 203362_s_at MAD2L1 Hs.591697 NM_002358 0.8095 66 202666_s_at ACTL6A Hs.435326 NM_004301 0.2162 67 204892_x_at EEF1A1 Hs.520703 NM_001402 −0.9566 68 205682_x_at APOM Hs.534468 NM_019101 −1.0558 69 209714_s_at CDKN3 Hs.84113 AF213033 0.9594 70 218381_s_at U2AF2 Hs.528007 NM_007279 −0.0076 71 201947_s_at CCT2 Hs.189772 NM_006431 0.2632 72 212722_s_at JMJD6 Hs.514505 AK021780 0.0968 73 204825_at MELK Hs.184339 NM_014791 1.1379 74 203184_at FBN2 Hs.519294 NM_001999 0.7174 75 201266_at TXNRD1 Hs.708065 NM_003330 0.2610 76 202969_at DYRK2 Hs.173135 AI216690 0.2560 77 204817_at ESPL1 Hs.153479 NM_012291 0.4866 78 209523_at TAF2 Hs.122752 AK001618 0.3803 79 218491_s_at THYN1 Hs.13645 NM_014174 −1.3652 80 217363_x_at — — AL031313 −0.9838 81 218009_s_at PRC1 Hs.567385 NM_003981 1.6691 82 204026_s_at ZWINT Hs.591363 NM_007057 0.9942 83 218355_at KIF4A Hs.648326 NM_012310 1.1017 84 202153_s_at NUP62 Hs.574492 NM_016553 −0.0983 85 213011_s_at TPI1 Hs.524219 BF116254 0.1005 86 217966_s_at FAM129A Hs.518662 NM_022083 −2.4459 87 214782_at CTTN Hs.596164 AU155105 0.2306 88 217967_s_at FAM129A Hs.518662 AF288391 −2.7067 89 204649_at TROAP Hs.524399 NM_005480 0.1495 90 35671_at GTF3C1 Hs.371718 U02619 0.0169 91 213502_x_at LOC91316 Hs.148656 AA398569 −2.1336 92 221285_at ST8SIA2 Hs.302341 NM_006011 −0.9209 93 221519_at FBXW4 Hs.500822 AF281859 −1.1897 94 202551_s_at CRIM1 Hs.699247 BG546884 −2.0141 95 217138_x_at IGL@ Hs.449585 AJ249377 −1.0505

In the present specification, “Probe Set. ID” shows the ID number provided to each probe set putting together 11 to 20 probes immobilized on a substrate in a microarray manufactured by Affymetrix, Inc [trade name: GeneChip (registered trademark) System]. The nucleotide sequence of the nucleic acid (probe set) shown by the above Probe Set. ID is easily available in database disclosed in Affymetrix's web page (database updated on Jun. 30, 2009). “UniGene. ID” shows the ID number of UniGene that is a database published by NCBI. GenBank accession number shows the accession number of the published database GenBank used for designing the sequence of each probe immobilized on a substrate in the above microarray manufactured by Affymetrix, Inc. [trade name: GeneChip (registered trademark) System].

In the present specification, the phrase “the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2” refers to the expression level of a gene having the nucleic acid shown by the GenBank accession number described in Table 1-1 and Table 1-2 corresponding to the Probe Set. ID described in Table 1-1 and Table 1-2. GenBank is a database provided by National Center for Biotechnology Information, and is available for general use. Moreover, the sequence to which the GenBank accession number described in Table 1-1 and Table 1-2 is provided is available from the above database. In addition, the above GenBank accession number shows the number in the latest release as of Jun. 30, 2009. In the present specification, a “gene” may be a unit or part of the nucleotide sequence from which RNA is derived as a gene transcript, and is a concept also including EST (expressed sequence tag).

In the examination method of the present invention, first, RNA is extracted from a specimen collected from a subject [step (A)].

The “subject” refers to a breast cancer patient and a patient suspected of having breast cancer. Here, the breast cancer patient is not particularly limited. The breast cancer patient includes, for example, a node-negative and ER-positive breast cancer patient, and the like. The node-negative and ER-positive breast cancer patient may be a node-negative and ER-positive breast cancer patient treated with hormonal therapy in which an antiestrogen is administered to the patient.

In the examination method of the present invention, for example, in the case where the subject is a node-negative and ER-positive breast cancer patient treated with hormonal therapy that administers an antiestrogen, the prediction such that the patient has good prognosis can be made with a high accuracy. In the present specification, “good prognosis” refers that no recurrence is found for 10 years after surgery.

The specimen includes, for example, a tumor tissue excised during surgery, a specimen collected from a subject by biopsy, and the like.

RNA extraction from a specimen can be performed by a known method. In addition, a commercial kit for extracting RNA can be also used for RNA extraction from a specimen. Here, the commercial kit includes, for example, trade name: Qiagen RNeasy kit (registered trademark), manufactured by Qiagen, and the like.

Next, a determination sample is prepared by using the RNA extracted in the step (A) [step (B)].

In the step (B), a determination sample suitable for determining the gene expression level, in other words, the production amount of transcripts corresponding to the gene (cRNA, cDNA, mRNA, and the like) is prepared. Specifically, the determination sample can be prepared by, for example, amplification of the corresponding cRNA or cDNA using the RNA extracted in the above step (A), purification of mRNA from the RNA extracted in the above step (A), or the like. In addition, in the present invention, when it is possible to determine the gene expression level, the RNA extracted in the above step (A) may be directly used as a determination sample.

Amplification of the cRNA can be performed by using a known method. A commercial kit for amplifying cRNA can be also used for the cRNA amplification. Here, the commercial kit includes, for example, trade name: One-Cycle Target Labeling and Control Reagents, manufactured by Affymetrix, Inc., and the like. In addition, the amplification of the cDNA can be performed by using a known method. A commercial kit for amplifying cDNA can be also used for the cDNA amplification. Purification of the mRNA can be performed by using a known purification method. In addition, a commercial kit may be also used for the mRNA purification.

Next, the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2 is determined using the determination sample obtained in the step (B) [step (C)}].

In the step (C), for example, a microarray, quantitative RT-PCR, quantitative PCR, and the like can be used for determination of the expression level. Among them, it is preferable to use a microarray for determination, since the expression level can be rapidly and easily determined. In this case, the fluorescence intensity in the microarray may be directly used as the expression level in the following step. The determination of the expression level by a microarray can be performed by using a known method.

The expression level of each gene in the gene groups described in Table 1-1 and Table 1-2 can be determined by utilizing, for example, the nucleic acids (probe sets shown by Probe Set. ID) described in Table 1-1 and Table 1-2. In the examination method of the present invention, the nucleic acids (probe sets) described in Table 1-1 and Table 1-2 are used as a prognosis factor in the examination of prognosis of breast cancer. The nucleic acids (probe sets) are found by the present inventors as those having the great effects in the examination of prognosis of breast cancer in many cases. In addition, the number of the nucleic acids (probe sets) used in the prognosis factor in the examination method of the present invention is 95 and is considered as the number that gives the highest accuracy of the examination. Therefore, prognosis can be properly predicted for various cases according to the examination method of the present invention.

In the examination method of the present invention, when the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2 is determined by using a microarray, for example, a microarray having at least the nucleic acids (probe sets) described in Table 1-1 and Table 1-2, and the like can be used as the microarray. The microarray includes, for example, trade name: Human Genome U133 Plus 2.0 Array, manufactured by Affymetrix, Inc., and the like. For example, when trade name: Human Genome U133 Plus 2.0 Array, manufactured by Affymetrix, Inc. described above is used, the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2 can be determined at a time by the 95 nucleic acids (probe sets) shown by the Probe Set. ID described in Table 1-1 and Table 1-2.

Next, the expression level determined in the step (C) is analyzed [step (D)]. Thereafter, prognosis of breast cancer is examined, based on the analysis result obtained in the step (D) [step (E)].

In the step (D), the expression level can be analyzed by using, for example, a classification method, a hierarchical cluster analysis, and a scoring method. Here, as the expression level, one obtained by normalizing the determined raw data of the expression level, for example, by RMA algorithm, MAS5 algorithm, PLIER algorithm, or the like can be used. The RMA algorithm is available, for example, on the analysis software (manufactured by Affymetrix, Inc., trade name: Affymetrix Expression Console software).

As the classification method, a known method can be used. The classification method includes, for example, Between-group analysis (BGA) (see Culhane, A. C. et al., Bioinformatics, 2002, Vol. 18, pp. 1600-1608, “Between-group analysis of microarray data”), support vector machine (SVM), diagonal linear discriminant analysis (DLDA), k nearest neighbor classification (kNN), decision tree, Random Forest, neural net, and the like. Among them, BGA is preferable from the viewpoint of good classification of subjects into those predicted as good prognosis and those predicted as poor prognosis. In the case where the expression level is analyzed by using the classification method, those predicted as good prognosis and those predicted as poor prognosis based on the expression level are classified. Therefore, in this case, in the step (E), prognosis of breast cancer can be predicted according to the result of the classification. In the case where the BGA is used, a discriminant is constructed. Prognosis of breast cancer may be predicted based on solution D of discriminant.

The discriminant includes a discriminant represented by the following formula (1):

$\begin{matrix} {D = {\frac{\sum\limits_{i}{w_{i}X_{i}}}{\sum\limits_{i}X_{i}} - 0.0061}} & (1) \end{matrix}$

wherein “i” in the formula (1) shows the gene number provided to the nucleic acid described in Table 1-1 and Table 1-2, “w_(i)” in the formula (1) shows a weight coefficient corresponding to the nucleic acid with gene number i described in Table 1-1 and Table 1-2, “X_(i)” in the formula (1) shows a normalized expression level which is obtained by normalization using the following formula (2): X _(i) =y _(i)+abs[round{min(y _(ij))−1}]  (2) and

$``\sum\limits_{i}"$ in the formula (1) shows the summation of each nucleic acid, and wherein “j” in the formula (2) shows the specimen number provided to each specimen, “y_(ij)” in the formula (2) shows the standardized expression level in a specimen with specimen number j of a gene corresponding to the nucleic acid with gene number i, “min” in the formula (2) shows the minimum value of the value in parentheses, “round” in the formula (2) shows the value obtained by rounding the value in parentheses to the nearest whole number, “abs” in the formula (2) shows the absolute value of the value in parentheses, and “y_(i)” in the formula (2) shows a standardized expression level of a gene corresponding to the nucleic acid with gene number i, the standardized expression level being obtained by standardization using the following formula (3): y _(i) =x _(i) −u _(i)  (3) wherein “x_(i)” in the formula (3) shows the expression level of a gene corresponding to the nucleic acid with gene number i, and “u_(i)” in the formula (3) shows the average value of specimens of the expression level of a gene corresponding to the nucleic acid with gene number i. In the case where the expression level is analyzed using the discriminant, the value of the expression level in the specimen is assigned to x_(i) (i=1, 2, . . . , 95) of the discriminant in sequence, to calculate solution D. In this case, if solution D is a positive value, poor prognosis can be predicted, and if solution D is 0 or a negative value, good prognosis can be predicted, in the step (E).

The hierarchical cluster analysis can be performed by, for example, using the expression level data in a specimen collected from a subject (or data of fluorescence intensity associated with the expression level), the expression level data in a group of specimens which is already known as good prognosis (or data of fluorescence intensity associated with the expression level), and the expression level data in a group of specimens which is already known as poor prognosis (or data of fluorescence intensity associated with the expression level), to thereby calculate a distance showing the degree of similarity between specimens based on the expression level (or data of fluorescence intensity associated with the expression level), forming various clusters based on this distance, integrating the clusters, and creating a dendrogram. Here, the distance includes, for example, Spearman's rank correlation coefficient, Euclidean distance, and the like. In addition, the cluster integration can be performed by, for example, Ward's method, complete linkage method, centroid linkage method, or the like. Among them, by using Spearman's rank correlation coefficient and Ward's method, those predicted as good prognosis and those predicted as poor prognosis can be favorably classified. In this case, prognosis of breast cancer can be properly predicted according to the result of the hierarchical cluster analysis, in the step (E).

As the scoring method, a known method can be used. The scoring method includes, for example, principal component analysis, multiple regression analysis, logistic regression analysis, Partial Least Square, and the like. Among them, principal component analysis is preferable from the viewpoint of good classification of subjects into those predicted as good prognosis and those predicted as poor prognosis. In the case where the expression level is analyzed by using the scoring method, scoring is performed so as to classify into a score of a specimen predicted as good prognosis and a score of a specimen predicted as poor prognosis based on the expression level. Therefore, in this case, prognosis of breast cancer can be properly predicted according to the result of the scoring, in the step (E).

As described above, according to the method for examining prognosis of breast cancer of the present invention, since the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2 is analyzed, prognosis of breast cancer can be properly examined.

By using the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2, an indication for determining prognosis of breast cancer can be obtained and provided. The method for obtaining an indication for determining prognosis of breast cancer includes the steps of:

(a) extracting RNA from a specimen collected from a subject with breast cancer,

(b) preparing a determination sample using the RNA extracted in the step (a),

(c) determining the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2 using the determination sample obtained in the step (b),

(d) analyzing the expression level determined in the step (c), and

(e) obtaining an indication for the possibility of poor prognosis or indication for the possibility of good prognosis of breast cancer of the subject, based on the analysis result obtained in the step (d). The steps (a) to (d) can be performed in the same manner as in the steps (A) to (D) in the method for examining prognosis of breast cancer. Also, an indication for the possibility of poor prognosis or indication for the possibility of good prognosis of breast cancer of the subject can be obtained in the step (e) by the method used in the step (E) in the method for examining prognosis of breast cancer.

EXAMPLES

Hereinbelow, the present invention will be described in detail with reference to Examples. However, the present invention is not limited to these Examples.

Example 1

Data of 549 cases were extracted from node-negative and ER-positive cases from 6 datasets including accession numbers: GSE2034, GSE2990, GSE4922, GSE6532, GSE7390, and GSE9195 in NCBI Gene Expression Omnibus of microarray experiments.

In each data of the 549 cases, the expression level of each nucleic acid (probe set) on the microarray used in the data acquisition was normalized for every dataset by using an RMA algorithm of the analysis software (manufactured by Affymetrix, Inc., trade name: Affymetrix Expression Console software). Next, the average expression level value of the nucleic acid in the dataset was subtracted from the expression level value of the nucleic acid (probe set) on the array used in the data acquisition, to standardize the expression level of the nucleic acid, for every dataset.

Thereafter, zScore was calculated for each of the nucleic acids (probe sets) on the array, by using a package “GeneMeta v1.16.0” contained in an additional package “BioConductor” ver. 2.4 used in software for statistical analysis “R”, according to a literature by Jung Kyoon Choi et al., “Combining multiple microarray studies and modeling interstudy variation”, Bioinformatics, Vol. 19, Suppl. 1, 2003, pp. i84-i90. Then, the nucleic acids (probe sets) were arranged in order of the absolute value of the zScore.

Next, a discriminant was constructed according to Between-group analysis. In order to obtain an optimal accuracy, the number of probe sets optimal for the examination of prognosis of breast cancer was obtained by Sequential Forward Filtering method. Here, while increasing the selected number of the nucleic acids (probe sets) by 5 until reaching to 300, from the nucleic acids (probe sets), in order of the absolute value of the zScore, the nucleic acids (probe sets) were selected to construct a discriminant.

Using each obtained discriminant and each data of the 549 cases, an examination accuracy of each discriminant was validated by Leave-One-Out Cross-Validation. The examination accuracy was evaluated by obtaining the sensitivity and specificity of each discriminant, creating an ROC curve based on the sensitivity and specificity, and calculating the area under the curve of this ROC curve. Moreover, a relationship between the area under the curve of the ROC curve and the number of the nucleic acids (probe sets) was examined, thereby obtaining the number (the optimal number) of the nucleic acids (probe sets) which maximizes the area under the curve of the ROC curve.

The sensitivity was obtained by dividing the number of specimens determined as “recurrence” (poor prognosis) for 10 years after surgery based on the observation result and predicted as having “recurrence” (poor prognosis) according to the discriminant, by the number of specimens determined as “recurrence” (poor prognosis) for 10 years after surgery based on the observation result, and multiplying by 100. In addition, the specificity was obtained by dividing the number of specimens determined as “no recurrence” (good prognosis) for 10 years after surgery based on the observation result and predicted as having “no recurrence” (good prognosis) according to the discriminant, by the number of specimens determined as “no recurrence” (good prognosis) based on the observation result, and multiplying by 100.

A graph showing the result of examining a relationship between the number of probes and the area under the curve of the ROC curve in Example 1 is shown in FIG. 1.

From the result shown in FIG. 1, it is found that the area under the curve of the ROC curve reaches the largest, when the number of nucleic acids (probe sets) is 95. From this result, it is found that the examination accuracy also reaches the highest when the number of nucleic acids (probe sets) is 95. These 95 nucleic acids (probe sets) are as shown in Table 1-1 and Table 1-2.

In addition, zScores, expression tendencies in the recurrent group having recurrence for 10 years after surgery, and weight coefficients in the discriminant of these 95 nucleic acids (probe sets) are shown in Table 2-1 and Table 2-2.

TABLE 2-1 Expression Gene BGA Tendencies in Weight Number Probe Set. ID zScore Recurrent Group Coefficient 1 219306_at −5.3567 increase 0.5960 2 218585_s_at −5.1377 increase 1.4097 3 221677_s_at −5.0601 increase 0.4683 4 201088_at −4.9734 increase 1.0480 5 209034_at 4.9696 decrease −1.4059 6 202610_s_at −4.9048 increase −0.0393 7 218906_x_at −4.8323 increase 0.0880 8 212723_at −4.6998 increase 0.3141 9 222231_s_at −4.6324 increase 0.6264 10 208838_at −4.6253 increase 0.2207 11 218039_at −4.6029 increase 1.5846 12 209472_at 4.6020 decrease −1.7235 13 212898_at −4.5966 increase 0.1172 14 202620_s_at −4.5827 increase 1.3605 15 201059_at −4.5756 increase 0.3591 16 201841_s_at −4.5605 increase 1.2420 17 203755_at −4.5410 increase 0.9909 18 211750_x_at −4.5346 increase 0.0145 19 38158_at −4.5201 increase 0.5325 20 204709_s_at −4.5107 increase 0.0798 21 201589_at −4.4720 increase 0.3106 22 218460_at −4.4386 increase 0.0198 23 207430_s_at −4.4260 increase 1.9177 24 212139_at −4.4166 increase −0.0501 25 211596_s_at 4.4024 decrease −2.0999 26 212160_at −4.4006 increase 0.3461 27 219238_at 4.3966 decrease −1.2689 28 203432_at −4.3549 increase 0.4665 29 201377_at −4.3403 increase 0.1269 30 218875_s_at −4.3334 increase 0.1012 31 221922_at −4.3238 increase 0.4423 32 218727_at −4.2747 increase −0.0411 33 207469_s_at −4.2733 increase 0.8827 34 218483_s_at 4.2641 decrease −1.3198 35 204641_at −4.2552 increase 1.5825 36 219502_at −4.2547 increase −0.1883 37 209054_s_at −4.2423 increase 0.0465 38 220318_at −4.2376 increase 0.3073 39 210297_s_at −4.2337 increase 1.6681 40 209186_at −4.2333 increase 0.2014 41 219787_s_at −4.1833 increase 0.8181 42 45633_at −4.1827 increase −0.2363 43 200848_at 4.1800 decrease −1.5895 44 200822_x_at −4.1767 increase 0.0814 45 211072_x_at −4.1602 increase 0.0380 46 200811_at 4.1591 decrease −1.4620 47 202864_s_at 4.1381 decrease −1.3947 48 202154_x_at −4.1334 increase 0.1241 49 213152_s_at 4.1121 decrease −1.2495 50 209368_at 4.0924 decrease −1.8835

TABLE 2-2 51 211058_x_at −4.0877 increase 0.0646 52 209251_x_at −4.0829 increase 0.0453 53 213646_x_at −4.0808 increase 0.0396 54 204540_at −4.0657 increase 1.8487 55 202026_at 4.0646 decrease −1.3587 56 201090_x_at −4.0595 increase 0.0733 57 213119_at −4.0519 increase 0.0680 58 217840_at −4.0419 increase 0.0313 59 206559_x_at 4.0301 decrease −0.9727 60 202066_at −4.0298 increase 0.7385 61 203108_at −4.0225 increase 1.0799 62 218697_at −4.0184 increase −0.0693 63 222039_at −3.9873 increase 0.6820 64 202069_s_at −3.9868 increase 0.2302 65 203362_s_at −3.9840 increase 0.8095 66 202666_s_at −3.9742 increase 0.2162 67 204892_x_at 3.9593 decrease −0.9566 68 205682_x_at 3.9520 decrease −1.0558 69 209714_s_at −3.9454 increase 0.9594 70 218381_s_at −3.9424 increase −0.0076 71 201947_s_at −3.9400 increase 0.2632 72 212722_s_at −3.9357 increase 0.0968 73 204825_at −3.9323 increase 1.1379 74 203184_at −3.9252 increase 0.7174 75 201266_at −3.9251 increase 0.2610 76 202969_at −3.9203 increase 0.2560 77 204817_at −3.9002 increase 0.4866 78 209523_at −3.9002 increase 0.3803 79 218491_s_at 3.9000 decrease −1.3652 80 217363_x_at 3.8939 decrease −0.9838 81 218009_s_at −3.8933 increase 1.6691 82 204026_s_at −3.8818 increase 0.9942 83 218355_at −3.8817 increase 1.1017 84 202153_s_at −3.8766 increase −0.0983 85 213011_s_at −3.8763 increase 0.1005 86 217966_s_at 3.8759 decrease −2.4459 87 214782_at −3.8666 increase 0.2306 88 217967_s_at 3.8652 decrease −2.7067 89 204649_at −3.8617 increase 0.1495 90 35671_at −3.8585 increase 0.0169 91 213502_x_at 3.8571 decrease −2.1336 92 221285_at 3.8490 decrease −0.9209 93 221519_at 3.8432 decrease −1.1897 94 202551_s_at 3.8420 decrease −2.0141 95 217138_x_at 3.8260 decrease −1.0505

Based on the above result, the conclusive discriminant was constructed. The constructed discriminant is a discriminant represented by the following formula (1):

$\begin{matrix} {D = {\frac{\sum\limits_{i}{w_{i} \times X_{i}}}{\sum\limits_{i}X_{i}} - 0.0061}} & (1) \end{matrix}$ {in the formula (1), “i” shows the gene number provided to the nucleic acid described in Table 1-1 and Table 1-2, “w_(i)” shows a weight coefficient corresponding to the nucleic acid with gene number i described in Table 1-1 and Table 1-2, and “X_(i)” shows a normalized expression level which is obtained by normalization using the following formula (2): X _(i) =y _(i)+abs[round{min(y _(ij))−1}]  (2) [in the formula (2), “j” shows the specimen number provided to each specimen, “y_(ij)” shows the standardized expression level in a specimen with specimen number j of a gene corresponding to the nucleic acid with gene number i, “min” shows the minimum value of the value in parentheses, “round” shows the value obtained by rounding the value in parentheses to the nearest whole number, “abs” shows the absolute value of the value in parentheses, “y_(i)” shows a standardized expression level of a gene corresponding to the nucleic acid with gene number i, the standardized expression level being obtained by standardization using the following formula (3): y _(i) =x _(i) −u _(i)  (3) (in the formula (3), “x_(i)” shows the expression level of a gene corresponding to the nucleic acid with gene number i, and “u_(i)” shows the average value of specimens of the expression level of a gene corresponding to the nucleic acid with gene number) i.).] and Σ_(i) shows the summation of each nucleic acid.}.

Here, poor prognosis is predicted when solution D of the discriminant is a positive value, and good prognosis is predicted when solution D is 0 or a negative value.

Example 2 (1) Data Acquisition of Expression Level of Nucleic Acids (Probe Sets)

RNA was extracted from tumor tissues obtained at each surgery of 105 breast cancer patients by using an RNA extraction kit (manufactured by QIAGEN Sciences, trade name: Qiagen RNeasy mini kit).

The 105 breast cancer patients are node-negative and ER-positive patients who underwent breast conserving surgery followed by radiation therapy or mastectomy during the period 1996-2005. The age range of these patients is 30 to 83, and the median age is 54. Clinicopathological features of the 105 breast cancer patients are shown in Table 3.

TABLE 3 Number of Prediction by 95-gene classifier patients low-risk group high-risk group among 105 for recurrence for recurrence p- patients (good prognosis) (poor prognosis) value Postmenopausal 56 37 19 0.10 Tumor size T T = 1 58 37 21 0.20 T = 2 45 23 22 T = 3 2 1 1 T = 4 0 0 0 Histological 1 29 22 7 <0.01 Grade 2 62 36 26 3 14 3 11 Presence or positive 105 61 44 absence of ER negative 0 0 0 Presence or positive 87 52 35 0.45 absence of PR negative 18 9 9 Presence or positive 19 8 11 0.12 absence of HER2 negative 86 53 33 Ki67 positive 19 7 12 0.04 negative 86 54 32

The tumor size T is represented by four levels, 1 to 4, based on the determination result by diagnostic imaging such as mammography and ultrasound. Here, T=1 shows that the maximum size of the tumor is 2 cm or less, T=2 shows that the maximum size of the tumor is more than 2 cm and 5 cm or less, T=3 shows that the maximum size of the tumor is more than 5 cm, and T=4 shows that the tumor invades the chest wall or skin, regardless of the tumor size.

The histological grade is represented by three levels, 1 to 3, based on the total score of the score of the nuclear grade (Score 1: low frequency of nuclear pleomorphism, Score 2: moderate frequency of nuclear pleomorphism, Score 3: high frequency of nuclear pleomorphism), the score of the change rate of tissue structure (Score 1: <10%, Score 2: 10 to 75%, Score 3: >75%), and the score of the frequency of cell division (Score 1: 0-4 mitoses per 10 high power field (HPF), Score 2: 5-10 mitoses per 10 HPF, Score 3: 11≦mitoses per 10 high power field (HPF)). Here, conventionally, it has been considered that HG=1 has been Score 3 to 5 and has shown cancer with good prognosis, that HG=2 has been Score 6 to 7, and that HG=3 has been Score 8 to 9 and has shown cancer with the worst prognosis.

The presence or absence of ER is represented by positive and negative, based on the result of the immunostaining method. Conventionally, it has been generally considered that prognosis has been poor in the case of ER negative, and that prognosis has been good in the case of ER positive.

The presence or absence of PR is represented by positive and negative, based on the result of the immunostaining method. Conventionally, it has been generally considered that prognosis has been poor in the case of PR negative, and that prognosis has been good in the case of PR positive.

The presence or absence of HER2 is represented by positive and negative, based on the result of the immunostaining method. Conventionally, it has been is generally considered that prognosis has been poor in the case of HER2 positive, and that prognosis has been good in the case of HER2 negative.

The Ki67 is represented by positive and negative, based on the result of the immunostaining method. Conventionally, it has been generally considered that prognosis has been poor in the case of Ki67 positive, and that prognosis has been good in the case of Ki67 negative.

Next, cRNA was amplified, biotinylated, and fragmented, using 1 μg of the obtained RNA [RNA Integrity Number (RIN) value >6] and a kit for expression analysis (manufactured by Affymetrix, Inc., trade name: One-Cycle Target Labeling and Control Reagents).

The obtained fragmented biotin-labeled cRNA was hybridized over night with the nucleic acids (probe sets) on a human genome array for expression analysis (manufactured by Affymetrix, Inc., trade name: Human Genome U133 Plus 2.0 Array). Hybridization of the fragmented biotin-labeled cRNA with the nucleic acids (probe sets) on the array was performed according to the recommended conditions of the manufacturer (Affymetrix, Inc.).

Next, the array after hybridization was subjected to a machine specialized for wash and stain operation of microarrays (manufactured by Affymetrix, Inc., trade name: GeneChip Fluidics Station 450), thereby performing fluorescent staining of the cRNA hybridized with the nucleic acids (probe, sets) on the array and washing.

Thereafter, the array was subjected to a laser scanner [manufactured by Affymetrix, Inc., trade name: GeneChip (registered trademark) Scanner 3000], thereby reading a signal based on the fluorescently-labeled substance of the cRNA hybridized with the nucleic acids (probe sets) on the array was read, and quantifing the fluorescent intensity.

The obtained fluorescent intensity data was processed by a software [manufactured by Affymetrix, Inc., trade name: GeneChip (registered trademark) Operating Software], to obtain a CEL file.

The expression level data (fluorescent intensity data) of the nucleic acids (probe sets) in all 105 cases of breast cancer patients was normalized by using the obtained 105 cases of CEL file data and the RMA algorithm of the analysis software (manufactured by Affymetrix, Inc., trade name: Affymetrix Expression Console software).

(2) Validation of Performance of Discriminant

Next, whether all 105 cases of breast cancer patients would cause a recurrence was predicted by using the data after normalization obtained in the above (1) and the discriminant. Moreover, assuming the pathological observation result as the true value, the performance of the discriminant was evaluated by comparing the pathological observation result with the result predicted by the discriminant. The result of examining a relationship between the result predicted by the discriminant and the observation result for the 105 cases of breast cancer patients in Example 2 is shown in FIG. 2.

From the result shown in FIG. 2, it is found that, among all 105 cases, 61 cases of breast cancer patients were predicted as having “no recurrence,” and 44 cases of breast cancer patients were predicted as having “recurrence”. In other words, in the case where the discriminant is used, it is found that, for the 61 cases of breast cancer patients, good prognosis is predicted, and for the 44 cases of breast cancer patients, poor prognosis is predicted.

In addition, when the performance of the discriminant is evaluated assuming the pathological observation result as the true value, it is found that the sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) are 83.3%, 70.4%, 93.4%, and 45.5%, respectively.

Therefore, from these results, it is suggested that prognosis of breast cancer can be properly predicted by using the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2 and the discriminant.

In addition, the breast cancer patients predicted as having “no recurrence” according to the discriminant (low-risk group for recurrence) and the breast cancer patients predicted as having “recurrence” according to the discriminant (high-risk group for recurrence) were each observed after surgery. The recurrence-free survival rate was examined by Kaplan-Meier plot. In addition, the result was evaluated by the log-rank test. A relationship between the period after surgery and the recurrence-free survival rate in Example 2 is shown in FIG. 3.

From the result shown in FIG. 3, it is found that, while the 10-year recurrence-free survival rates after surgery are 53% in the high-risk group for recurrence (in the figure, “high-risk for recurrence”) and 93% in the low-risk group for recurrence (in the figure, “low-risk for recurrence”). In addition, since the log-rank test resulted in p=8.6×10⁻⁷, it is found that the low-risk group for recurrence shows significantly better prognosis than the high-risk group for recurrence.

Therefore, from these results, it is found that prognosis of breast cancer can be properly predicted with a high accuracy by using the expression level of the nucleic acids (probe sets) described in Table 1-1 and Table 1-2 and the discriminant.

Test Example 1

For each of an examination method using the 95 nucleic acids as a prognosis factor (Experimental Number 1), an examination method using patient's menopausal status as a prognosis factor (Experimental Number 2), an examination method using the tumor size as a prognosis factor (Experimental Number 3), an examination method using histological grade as a prognosis factor (Experimental Number 4), an examination method using the presence or absence of PR as a prognosis factor (Experimental Number 5), an examination method using the presence or absence of human epidermal growth factor receptor 2 (HER2) as a prognosis factor (Experimental Number 6), an examination method using whether the ratio of Ki67 positive cells in all cells is 20% or more as a prognosis factor (Experimental Number 7), and an examination method by Genomic Grade Index (GGI) using 97 genes that are different type from the 95 genes as a prognosis factor (Experimental Number 8), multivariate COX regression hazard analysis was performed by using an additional package “survival v2.35-4” used in a software for statistical analysis “R”. The GGI was obtained according to a literature of Sotiriou Christos et al. (Journal of the National Cancer Institute, 2006, Vol. 98, Issue 4, pp. 262-272).

The result is shown in Table 4. In the table, “95genes” shows the examination method using the 95 nucleic acids as a prognosis factor (Experimental Number 1), “Mens” being the examination method using patient's menopausal status as a prognosis factor (Experimental Number 2), “T” being the examination method using the tumor size as a prognosis factor (Experimental Number 3), “HG” being the examination method using histological grade as a prognosis factor (Experimental Number 4), “PgR” being the examination method using the presence or absence of PR as a prognosis factor (Experimental Number 5), “HER2” being the examination method using the presence or absence of HER2 as a prognosis factor (Experimental Number 6), “Ki67” being the examination method using whether the ratio of Ki67 positive cells in all cells is 20% or more as a prognosis factor (Experimental Number 7), and “sign.GGI” being the examination method by GGI using 97 genes that are different type from the 95 genes as a prognosis factor (Experimental Number 8). Each hazard ratio is the value calculated assuming the hazard in the case of falling under “reference” in the table as 1.0. The menopausal status is represented as premenopausal and postmenopausal. In addition, conventionally, prognosis is considered to be good when sign.GGI is low, and prognosis is considered to be poor when sign.GGI is high.

TABLE 4 Experimental Prognosis Multivariate Analysis Number Factor Reference Hazard Ratio p-value 1 95genes no recurrence 7.70 9.6E−04 ** 2 Mens premenopausal 1.32 0.5345 3 T T = 1 2.25 0.0275 * 4 HG HG = 1, 2 1.24 0.7046 5 PgR negative 0.56 0.2654 6 HER2 negative 2.21 0.0911 7 ki67 negative 0.65 0.4288 8 sign.GGI low 1.08 0.7796 * 5% significant ** 1% significant

From the result shown in Table 4, it is found that the examination method of the present invention is an examination method with a high accuracy as compared with other examination methods, since the examination method using the discriminant in which the 95 nucleic acids are used as a prognosis factor (Experimental Number 1; the examination method of the present invention) has a hazard ratio of 7.70 and a p-value of 9.6E-04.

Example 3

For the data after normalization obtained in (1) of Example 2, a hierarchical cluster analysis was performed using Spearman's rank correlation coefficient and Ward's method, to create a dendrogram. The result of performing a hierarchical cluster analysis of the expression level data of the nucleic acids (probe sets) in each of 105 cases of breast cancer patients in Example 3 is shown in FIG. 4. In FIG. 4, a heat map representing the expression level of the nucleic acids (probe sets) is shown in the left, and the determination result of recurrence for 10 years after surgery (in the figure, “recurrence”) and no recurrence for 10 years after surgery (in the figure, “recurrence-free”) by the observation result is shown in the right.

From the result shown in FIG. 4, it is found that the breast cancer patients determined as recurrence (poor prognosis) for 10 years after surgery by the observation result (many in the upper part) and the breast cancer patients determined as no recurrence (good prognosis) for 10 years after surgery by the observation result (many in the lower part) can be classified, with a bold line drawn so as to divide the dendrogram serving as a boundary. Therefore, from these results, it is found that prognosis of breast cancer can be predicted with a high accuracy, and that prognosis of breast cancer can be properly examined by performing a hierarchical cluster analysis with the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2.

Example 4

For the data after normalization obtained in (1) of Example 2, principal component analysis was performed by using the genes described in Table 1-1 and Table 1-2, to calculate a conversion coefficient of each gene. In addition, the first and second principal component scores were calculated. The conversion coefficients calculated in Example 4 are shown in Table 5-1 and Table 5-2. In addition, a scatter plot of the first principal component score and the second principal component score calculated based on the expression level data of each of 105 cases of breast cancer patients in Example 4 is shown in FIG. 5. In FIG. 5, “PCA1” shows the first principal component score, and “PCA2” shows the second principal component score. In the figure, open circles are the breast cancer patients determined as “recurrence” for 10 years after surgery based on the observation result, and crosses are the breast cancer patients determined as “no recurrence” for 10 years after surgery based on the observation result.

TABLE 5-1 Gene Conversion Coefficient Conversion Coefficient Num- Probe Set. in First Principal in Second Principal ber ID Component Component 1 219306_at −0.1316 0.0596 2 218585_s_at −0.2082 0.0956 3 221677_s_at −0.1003 0.0528 4 201088_at −0.1770 0.0207 5 209034_at 0.0568 0.0142 6 202610_s_at −0.0333 0.0120 7 218906_x_at −0.0339 −0.0056 8 212723_at −0.0831 −0.0100 9 222231_s_at −0.0986 −0.0266 10 208838_at −0.0603 −0.0105 11 218039_at −0.2359 0.0856 12 209472_at 0.0395 −0.0048 13 212898_at −0.0518 −0.0014 14 202620_s_at −0.1275 0.1119 15 201059_at −0.0494 −0.0264 16 201841_s_at −0.1071 −0.0930 17 203755_at −0.1917 0.0860 18 211750_x_at −0.0606 0.0188 19 38158_at −0.1045 0.0345 20 204709_s_at −0.1103 0.0549 21 201589_at −0.0860 0.0098 22 218460_at −0.0214 −0.0057 23 207430_s_at −0.2134 −0.6702 24 212139_at −0.0356 −0.0072 25 211596_s_at 0.1028 −0.0321 26 212160_at −0.1002 0.0415 27 219238_at 0.0325 −0.0196 28 203432_at −0.1295 0.0264 29 201377_at −0.0446 0.0259 30 218875_s_at −0.0859 0.0387 31 221922_at −0.1019 0.0721 32 218727_at −0.0210 0.0092 33 207469_s_at −0.1364 0.0176 34 218483_s_at 0.0522 −0.0110 35 204641_at −0.2144 0.1173 36 219502_at −0.0497 0.0123 37 209054_s_at −0.0536 0.0045 38 220318_at −0.0752 −0.0135 39 210297_s_at −0.1929 −0.6064 40 209186_at −0.0511 0.0198 41 219787_s_at −0.1676 0.0745 42 45633_at −0.0476 −0.0005 43 200848_at 0.0460 0.0000 44 200822_x_at −0.0596 0.0307 45 211072_x_at −0.0630 0.0205 46 200811_at 0.0714 −0.0371 47 202864_s_at 0.0152 0.0097 48 202154_x_at −0.0484 0.0101 49 213152_s_at 0.0489 0.0090 50 209368_at 0.1107 −0.0240

TABLE 5-2 Gene Conversion Coefficient Conversion Coefficient Num- Probe Set. in First Principal in Second Principal ber ID Component Component 51 211058_x_at −0.0613 0.0181 52 209251_x_at −0.0599 0.0193 53 213646_x_at −0.0602 0.0204 54 204540_at −0.1123 −0.1300 55 202026_at 0.0280 0.0288 56 201090_x_at −0.0636 0.0220 57 213119_at −0.0271 0.0071 58 217840_at −0.0341 −0.0169 59 206559_x_at 0.0283 −0.0050 60 202066_at −0.0868 −0.0215 61 203108_at −0.0801 −0.1074 62 218697_at −0.0095 −0.0034 63 222039_at −0.1277 0.0404 64 202069_s_at −0.0669 0.0043 65 203362_s_at −0.2108 0.0775 66 202666_s_at −0.0790 0.0103 67 204892_x_at 0.0297 −0.0038 68 205682_x_at 0.0261 0.0025 69 209714_s_at −0.1942 0.0663 70 218381_s_at −0.0337 0.0084 71 201947_s_at −0.0800 −0.0273 72 212722_s_at −0.0688 −0.0040 73 204825_at −0.1787 0.0849 74 203184_at −0.0520 −0.0363 75 201266_at −0.0772 0.0104 76 202969_at −0.0490 0.0042 77 204817_at −0.1072 0.0286 78 209523_at −0.0977 0.0404 79 218491_s_at 0.0594 −0.0088 80 217363_x_at 0.0168 0.0061 81 218009_s_at −0.2386 0.0817 82 204026_s_at −0.1952 0.0760 83 218355_at −0.1904 0.0697 84 202153_s_at −0.0384 0.0261 85 213011_s_at −0.0634 0.0331 86 217966_s_at 0.0966 −0.0543 87 214782_at −0.0334 −0.0120 88 217967_s_at 0.1200 −0.0865 89 204649_at −0.0783 0.0241 90 35671_at −0.0225 −0.0215 91 213502_x_at 0.0521 0.0383 92 221285_at 0.0076 −0.0023 93 221519_at 0.0428 −0.0122 94 202551_s_at 0.1254 −0.0316 95 217138_x_at 0.0165 0.0017 Constant Term −1.2329E−17 −9.3129E−17

From the result shown in FIG. 5, it is found that the breast cancer patients determined as “no recurrence” (good prognosis) for 10 years after surgery based on the observation result and the breast cancer patients determined as “recurrence” (poor prognosis) for 10 years after surgery based on the observation result can be classified, with a point in the horizontal axis where the first principal component score is 0 serving as a boundary.

Therefore, from these results, it is found that prognosis of breast cancer can be predicted with a high accuracy, and prognosis of breast cancer can be properly examined by performing principal component analysis using the expression level of each gene in the gene groups described in Table 1-1 and Table 1-2.

The present invention can be embodiment in any other forms without departing from the spirit or essential characteristics of the invention. Therefore, the above-described examples are merely illustration in all aspects, and should not be understood to be limited thereto. The scope of the present invention is given in the claims, and is not bound to the description of the specification. Further, all modifications and changes belonging to the scope of equivalency of the claims are intended to be embraced within the scope of the present invention.

CITATION LIST Patent Literature

-   Published Japanese Translation of PCT International Publication for     Patent Application No. 2009-131262 -   Publication of Unexamined Patent Application No. 2007-528218

Non Patent Literature

-   Sotiriou Christos et al., “Gene expression profiling in breast     cancer: understanding the molecular basis of histologic grade to     improve prognosis.”, Journal of the National Cancer Institute,     published on Feb. 15, 2006, Vol. 98, Issue 4, pp. 262-272 

The invention claimed is:
 1. A method for examining prognosis of breast cancer comprising the steps of: (A) extracting RNA from a specimen collected from a subject, (B) preparing a determination sample using the RNA extracted in the step (A), (C) determining the expression level of each gene in the gene groups described in Table 1-1-1 and Table 1-2-1 using the determination sample obtained in the step (B), (D) analyzing the expression level of each gene determined in the step (C) comprising calculating solution D of a discriminant using the expression level and the discriminant is represented by the following formula (1): $\begin{matrix} {{D = {\frac{\sum\limits_{i}{w_{i} \times X_{i}}}{\sum\limits_{i}X_{i}} - 0.0061}},} & (1) \end{matrix}$ (E) examining prognosis of breast cancer, based on the analysis result obtained in the step (D), and predicting a poor prognosis when the solution D of the discriminant is a positive value, and a good prognosis when the solution D is 0 or a negative value, wherein i in the formula (1) shows a gene number provided to the nucleic acid described in Table 1-1 and Table 1-2, w_(i) in the formula (1) shows a weight coefficient corresponding to the nucleic acid with gene number i described in Table 1-1 and Table 1-2, and X_(i) in the formula (1) shows a normalized expression level which is obtained by normalization using the following formula (2): X _(i) =y _(i)+abs[round{min(y _(ij))−1}]  (2) and Σ_(i) in the formula (1) shows the summation of each nucleic acid, and wherein j in the formula (2) shows the specimen number provided to each specimen, y_(ij) in the formula (2) shows the standardized expression level in a specimen with specimen number j of a gene corresponding to the nucleic acid with gene number i, min in the formula (2) shows the minimum value of the value in parentheses, round in the formula (2) shows the value obtained by rounding the value in parentheses to the nearest whole number, abs in the formula (2) shows the absolute value of the value in parentheses, and y_(i) in the formula (2) shows a standardized expression level of a gene corresponding to the nucleic acid with gene number i, the standardized expression level being obtained by standardization using the following formula (3): y _(i) =x _(i) −u _(i)  (3) wherein x_(i) in the formula (3) shows the expression level of a gene corresponding to the nucleic acid with gene number i, and u_(i) in the formula (3) shows the average value of specimens of the expression level of a gene corresponding to the nucleic acid with gene number I, wherein Table 1-1-1 and Table 1-2-1 are: TABLE 1-1-1 GenBank Gene Probe Set. Gene Accession Number ID Symbol UniGene.ID Number 1 219306_at KIF15 Hs.646856 NM_020242 2 218585_s_at DTL Hs.656473 NM_016448 3 221677_s_at DONSON Hs.436341 AF232674 4 201088_at KPNA2 Hs.594238 NM_002266 5 209034_at PNRC1 Hs.75969 AF279899 6 202610_s_at MED14 Hs.407604 AF135802 7 218906_x_at KLC2 Hs.280792 NM_022822 8 212723_at JMJD6 Hs.514505 AK021780 9 222231_s_at LRRC59 Hs.370927 AK025328 10 208838_at CAND1 Hs.546407 AB020636 11 218039_at NUSAP1 Hs.615092 NM_016359 12 209472_at CCBL2 Hs.481898 BC000819 13 212898_at KIAA0406 Hs.655481 AB007866 14 202620_s_at PLOD2 Hs.477866 NM_000935 15 201059_at CTTN Hs.596164 NM_005231 16 201841_s_at HSPB1 Hs.520973 NM_001540 17 203755_at BUB1B Hs.631699 NM_001211 18 211750_x_at TUBA1C Hs.719091 BC005946 19 38158_at ESPL1 Hs.153479 D79987 20 204709_s_at KIF23 Hs.270845 NM_004856 21 201589_at SMC1A Hs.211602 D80000 22 218460_at HEATR2 Hs.535896 NM_017802 23 207430_s_at MSMB Hs.255462 NM_002443 24 212139_at GCN1L1 Hs.298716 D86973 25 211596_s_at LRIG1 Hs.518055 AB050468 26 212160_at XPOT Hs.85951 AI984005 27 219238_at PIGV Hs.259605 NM_017837 28 203432_at TMPO Hs.11355 AW272611 29 201377_at UBAP2L Hs.490551 NM_014847 30 218875_s_at FBXO5 Hs.520506 NM_012177 31 221922_at GPSM2 Hs.584901 AW195581 32 218727_at SLC38A7 Hs.10499 NM_018231 33 207469_s_at PIR Hs.495728 NM_003662 34 218483_s_at C11orf60 Hs.533738 NM_020153 35 204641_at NEK2 Hs.153704 NM_002497 36 219502_at NEIL3 Hs.405467 NM_018248 37 209054_s_at WHSC1 Hs.113876 AF083389 38 220318_at EPN3 Hs.670090 NM_017957 39 210297_s_at MSMB Hs.255462 U22178 40 209186_at ATP2A2 Hs.506759 M23114 41 219787_s_at ECT2 Hs.518299 NM_018098 42 45633_at GINS3 Hs.47125 AI421812 43 200848_at AHCYL1 Hs.705418 AA479488 44 200822_x_at TPI1 Hs.524219 NM_000365 45 211072_x_at TUBA1B Hs.719075 BC006481 46 200811_at CIRBP Hs.634522 NM_001280 47 202864_s_at SP100 Hs.369056 NM_003113 48 202154_x_at TUBB3 Hs.511743 NM_006086 49 213152_s_at SFRS2B Hs.476680 AI343248 50 209368_at EPHX2 Hs.212088 AF233336

TABLE 1-2-1 GenBank Gene Probe Set. Gene Accession Number ID Symbol UniGene.ID Number 51 211058_x_at TUBA1B Hs.719075 BC006379 52 209251_x_at TUBA1C Hs.719091 BC004949 53 213646_x_at TUBA1B Hs.719075 BE300252 54 204540_at EEF1A2 Hs.433839 NM_001958 55 202026_at SDHD Hs.719164 NM_003002 56 201090_x_at TUBA1B Hs.719075 NM_006082 57 213119_at SLC36A1 Hs.269004 AW058600 58 217840_at DDX41 Hs.484288 NM_016222 59 206559_x_at EEF1A1 — NM_001403 60 202066_at PPF1A1 Hs.530749 AA195259 61 203108_at GPRC5A Hs.631733 NM_003979 62 218697_at NCKIPSD Hs.655006 NM_016453 63 222039_at KIF18B Hs.135094 AA292789 64 202069_s_at IDH3A Hs.591110 AI826060 65 203362_s_at MAD2L1 Hs.591697 NM_002358 66 202666_s_at ACTL6A Hs.435326 NM_004301 67 204892_x_at EEF1A1 Hs.520703 NM_001402 68 205682_x_at APOM Hs.534468 NM_019101 69 209714_s_at CDKN3 Hs.84113 AF213033 70 218381_s_at U2AF2 Hs.528007 NM_007279 71 201947_s_at CCT2 Hs.189772 NM_006431 72 212722_s_at JMJD6 Hs.514505 AK021780 73 204825_at MELK Hs.184339 NM_014791 74 203184_at FBN2 Hs.519294 NM_001999 75 201266_at TXNRD1 Hs.708065 NM_003330 76 202969_at DYRK2 Hs.173135 AI216690 77 204817_at ESPL1 Hs.153479 NM_012291 78 209523_at TAF2 Hs.122752 AK001618 79 218491_s_at THYN1 Hs.13645 NM_014174 80 217363_x_at — — AL031313 81 218009_s_at PRC1 Hs.567385 NM_003981 82 204026_s_at ZWINT Hs.591363 NM_007057 83 218355_at KIF4A Hs.648326 NM_012310 84 202153_s_at NUP62 Hs.574492 NM_016553 85 213011_s_at TPI1 Hs.524219 BF116254 86 217966_s_at FAM129A Hs.518662 NM_022083 87 214782_at CTTN Hs.596164 AU155105 88 217967_s_at FAM129A Hs.518662 AF288391 89 204649_at TROAP Hs.524399 NM_005480 90 35671_at GTF3C1 Hs.371718 U02619 91 213502_x_at LOC91316 Hs.148656 AA398569 92 221285_at ST8SIA2 Hs.302341 NM_006011 93 221519_at FBXW4 Hs.500822 AF281859 94 202551_s_at CRIM1 Hs.699247 BG546884 95 217138_x_at IGL@ Hs.449585 AJ249377,

and wherein Table 1-1 and Table 1-2 are: TABLE 1-1 Gene Number Probe Set. ID Gene Symbol UniGene.ID 1 219306_at KIF15 Hs.646856 2 218585_s_at DTL Hs.656473 3 221677_s_at DONSON Hs.436341 4 201088_at KPNA2 Hs.594238 5 209034_at PNRC1 Hs.75969 6 202610_s_at MED14 Hs.407604 7 218906_x_at KLC2 Hs.280792 8 212723_at JMJD6 Hs.514505 9 222231_s_at LRRC59 Hs.370927 10 208838_at CAND1 Hs.546407 11 218039_at NUSAP1 Hs.615092 12 209472_at CCBL2 Hs.481898 13 212898_at KIAA0406 Hs.655481 14 202620_s_at PLOD2 Hs.477866 15 201059_at CTTN Hs.596164 16 201841_s_at HSPB1 Hs.520973 17 203755_at BUB1B Hs.631699 18 211750_x_at TUBA1C Hs.719091 19 38158_at ESPL1 Hs.153479 20 204709_s_at KIF23 Hs.270845 21 201589_at SMC1A Hs.211602 22 218460_at HEATR2 Hs.535896 23 207430_s_at MSMB Hs.255462 24 212139_at GCN1L1 Hs.298716 25 211596_s_at LRIG1 Hs.518055 26 212160_at XPOT Hs.85951 27 219238_at PIGV Hs.259605 28 203432_at TMPO Hs.11355 29 201377_at UBAP2L Hs.490551 30 218875_s_at FBXO5 Hs.520506 31 221922_at GPSM2 Hs.584901 32 218727_at SLC38A7 Hs.10499 33 207469_s_at PIR Hs.495728 34 218483_s_at C11orf60 Hs.533738 35 204641_at NEK2 Hs.153704 36 219502_at NEIL3 Hs.405467 37 209054_s_at WHSC1 Hs.113876 38 220318_at EPN3 Hs.670090 39 210297_s_at MSMB Hs.255462 40 209186_at ATP2A2 Hs.506759 41 219787_s_at ECT2 Hs.518299 42 45633_at GINS3 Hs.47125 43 200848_at AHCYL1 Hs.705418 44 200822_x_at TPI1 Hs.524219 45 211072_x_at TUBA1B Hs.719075 46 200811_at CIRBP Hs.634522 47 202864_s_at SP100 Hs.369056 48 202154_x_at TUBB3 Hs.511743 49 213152_s_at SFRS2B Hs.476680 50 209368_at EPHX2 Hs.212088 GenBank Weight Gene Number Accession Number Coefficient 1 NM_020242 0.5960 2 NM_016448 1.4097 3 AF232674 0.4683 4 NM_002266 1.0480 5 AF279899 −1.4059 6 AF135802 −0.0393 7 NM_022822 0.0880 8 AK021780 0.3141 9 AK025328 0.6264 10 AB020636 0.2207 11 NM_016359 1.5846 12 BC000819 −1.7235 13 AB007866 0.1172 14 NM_000935 1.3605 15 NM_005231 0.3591 16 NM_001540 1.2420 17 NM_001211 0.9909 18 BC005946 0.0145 19 D79987 0.5325 20 NM_004856 0.0798 21 D80000 0.3106 22 NM_017802 0.0198 23 NM_002443 1.9177 24 D86973 −0.0501 25 AB050468 −2.0999 26 AI984005 0.3461 27 NM_017837 −1.2689 28 AW272611 0.4665 29 NM_014847 0.1269 30 NM_012177 0.1012 31 AW195581 0.4423 32 NM_018231 −0.0411 33 NM_003662 0.8827 34 NM_020153 −1.3198 35 NM_002497 1.5825 36 NM_018248 −0.1883 37 AF083389 0.0465 38 NM_017957 0.3073 39 U22178 1.6681 40 M23114 0.2014 41 NM_018098 0.8181 42 AI421812 −0.2363 43 AA479488 −1.5895 44 NM_000365 0.0814 45 BC006481 0.0380 46 NM_001280 −1.4620 47 NM_003113 −1.3947 48 NM_006086 0.1241 49 AI343248 −1.2495 50 AF233336 −1.8835

TABLE 1-2 Gene Number Probe Set. ID Gene Symbol UniGene.ID 51 211058_x_at TUBA1B Hs.719075 52 209251_x_at TUBA1C Hs.719091 53 213646_x_at TUBA1B Hs.719075 54 204540_at EEF1A2 Hs.433839 55 202026_at SDHD Hs.719164 56 201090_x_at TUBA1B Hs.719075 57 213119_at SLC36A1 Hs.269004 58 217840_at DDX41 Hs.484288 59 206559_x_at EEF1A1 — 60 202066_at PPF1A1 Hs.530749 61 203108_at GPRC5A Hs.631733 62 218697_at NCKIPSD Hs.655006 63 222039_at KIF18B Hs.135094 64 202069_s_at IDH3A Hs.591110 65 203362_s_at MAD2L1 Hs.591697 66 202666_s_at ACTL6A Hs.435326 67 204892_x_at EEF1A1 Hs.520703 68 205682_x_at APOM Hs.534468 69 209714_s_at CDKN3 Hs.84113 70 218381_s_at U2AF2 Hs.528007 71 201947_s_at CCT2 Hs.189772 72 212722_s_at JMJD6 Hs.514505 73 204825_at MELK Hs.184339 74 203184_at FBN2 Hs.519294 75 201266_at TXNRD1 Hs.708065 76 202969_at DYRK2 Hs.173135 77 204817_at ESPL1 Hs.153479 78 209523_at TAF2 Hs.122752 79 218491_s_at THYN1 Hs.13645 80 217363_x_at — — 81 218009_s_at PRC1 Hs.567385 82 204026_s_at ZWINT Hs.591363 83 218355_at KIF4A Hs.648326 84 202153_s_at NUP62 Hs.574492 85 213011_s_at TPI1 Hs.524219 86 217966_s_at FAM129A Hs.518662 87 214782_at CTTN Hs.596164 88 217967_s_at FAM129A Hs.518662 89 204649_at TROAP Hs.524399 90 35671_at GTF3C1 Hs.371718 91 213502_x_at LOC91316 Hs.148656 92 221285_at ST8SIA2 Hs.302341 93 221519_at FBXW4 Hs.500822 94 202551_s_at CRIM1 Hs.699247 95 217138_x_at IGL@ Hs.449585 GenBank Weight Gene Number Accession Number Coefficient 51 BC006379 0.0646 52 BC004949 0.0453 53 BE300252 0.0396 54 NM_001958 1.8487 55 NM_003002 −1.3587  56 NM_006082 0.0733 57 AW058600 0.0680 58 NM_016222 0.0313 59 NM_001403 −0.9727  60 AA195259 0.7385 61 NM_003979 1.0799 62 NM_016453 −0.0693  63 AA292789 0.6820 64 AI826060 0.2302 65 NM_002358 0.8095 66 NM_004301 0.2162 67 NM_001402 −0.9566  68 NM_019101 −1.0558 69 AF213033 0.9594 70 NM_007279 −0.0076  71 NM_006431 0.2632 72 AK021780 0.0968 73 NM_014791 1.1379 74 NM_001999 0.7174 75 NM_003330 0.2610 76 AI216690 0.2560 77 NM_012291 0.4866 78 AK001618 0.3803 79 NM_014174 −1.3652  80 AL031313 −0.9838  81 NM_003981 1.6691 82 NM_007057 0.9942 83 NM_012310 1.1017 84 NM_016553 −0.0983 85 BF116254 0.1005 86 NM_022083 −2.4459  87 AU155105 0.2306 88 AF288391 −2.7067  89 NM_005480 0.1495 90 U02619 0.0169 91 AA398569 −2.1336  92 NM_006011 −0.9209  93 AF281859 −1.1897  94 BG546884 −2.0141  95 AJ249377 −1.0505.  


2. The method for examining prognosis of breast cancer according to claim 1, wherein the expression level is determined by using a microarray having at least the nucleic acid described in Table 1-1-1 and Table 1-2-1. 